KMS Of Academy of mathematics and systems sciences, CAS
Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation | |
Ding, Chao1; Qi, Hou-Duo2 | |
2017 | |
发表期刊 | COMPUTATIONAL OPTIMIZATION AND APPLICATIONS |
ISSN | 0926-6003 |
卷号 | 66期号:1页码:187-218 |
摘要 | One of the challenging problems in collaborative position localization arises when the distance measurements contain non-line-of-sight (NLOS) biases. Convex optimization has played a major role in modelling such problems and numerical algorithm developments. One of the successful examples is the semi-definite programming (SDP), which translates Euclidean distances into the constraints of positive semidefinite matrices, leading to a large number of constraints in the case of NLOS biases. In this paper, we propose a new convex optimization model that is built upon the concept of Euclidean distance matrix (EDM). The resulting EDM optimization has an advantage that its Lagrangian dual problem is well structured and hence is conducive to algorithm developments. We apply a recently proposed 3-block alternating direction method of multipliers to the dual problem and tested the algorithm on some real as well as simulated data of large scale. In particular, the EDM model significantly outperforms the existing SDP model and several others. |
关键词 | Euclidean distance matrix Collaborative localization Non-line of sight (NLOS) Augmented Lagrangian Alternating direction method of multipliers (ADMM) |
DOI | 10.1007/s10589-016-9858-5 |
语种 | 英语 |
资助项目 | Engineering and Physical Science Research Council (UK)[EP/K007645/1] |
WOS研究方向 | Operations Research & Management Science ; Mathematics |
WOS类目 | Operations Research & Management Science ; Mathematics, Applied |
WOS记录号 | WOS:000391453500007 |
出版者 | SPRINGER |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.amss.ac.cn/handle/2S8OKBNM/24474 |
专题 | 应用数学研究所 |
通讯作者 | Ding, Chao |
作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing, Peoples R China 2.Univ Southampton, Sch Math, Southampton SO17 1BJ, Hants, England |
推荐引用方式 GB/T 7714 | Ding, Chao,Qi, Hou-Duo. Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation[J]. COMPUTATIONAL OPTIMIZATION AND APPLICATIONS,2017,66(1):187-218. |
APA | Ding, Chao,&Qi, Hou-Duo.(2017).Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation.COMPUTATIONAL OPTIMIZATION AND APPLICATIONS,66(1),187-218. |
MLA | Ding, Chao,et al."Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation".COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 66.1(2017):187-218. |
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